



import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel



def      _lowerCAmelCase			(   __lowerCAmelCase    ,    __lowerCAmelCase    ,    __lowerCAmelCase )  ->   Optional[int]:


				"""simple docstring"""

				snake_case__      : str							=      OmegaConf.load(UpperCamelCase__ )
				snake_case__      : Tuple							=      torch.load(UpperCamelCase__    ,    map_location='''cpu''' )['''model''']
				snake_case__      : List[str]							=      list(state_dict.keys() )

				# extract state_dict for VQVAE
				snake_case__      : Any							=      {}
				snake_case__      : Dict							=      '''first_stage_model.'''
				for key in keys:
								if key.startswith(UpperCamelCase__ ):
												snake_case__      : List[str]							=      state_dict[key]

    # extract state_dict for UNetLDM
				snake_case__      : Dict							=      {}
				snake_case__      : Tuple							=      '''model.diffusion_model.'''
				for key in keys:
								if key.startswith(UpperCamelCase__ ):
												snake_case__      : Optional[int]							=      state_dict[key]

				snake_case__      : Tuple							=      config.model.params.first_stage_config.params
				snake_case__      : Optional[Any]							=      config.model.params.unet_config.params

				snake_case__      : Dict							=      VQModel(**UpperCamelCase__ ).eval()
				vqvae.load_state_dict(UpperCamelCase__ )

				snake_case__      : str							=      UNetLDMModel(**UpperCamelCase__ ).eval()
				unet.load_state_dict(UpperCamelCase__ )

				snake_case__      : int							=      DDIMScheduler(
				    timesteps=config.model.params.timesteps    ,    beta_schedule='''scaled_linear'''    ,    beta_start=config.model.params.linear_start    ,    beta_end=config.model.params.linear_end    ,    clip_sample=UpperCamelCase__    ,    )

				snake_case__      : Any							=      LDMPipeline(UpperCamelCase__    ,    UpperCamelCase__    ,    UpperCamelCase__ )
				pipeline.save_pretrained(UpperCamelCase__ )


if __name__ == "__main__":
				A__								=		argparse.ArgumentParser()
				parser.add_argument('''--checkpoint_path''', type=str, required=True)
				parser.add_argument('''--config_path''', type=str, required=True)
				parser.add_argument('''--output_path''', type=str, required=True)
				A__								=		parser.parse_args()

				convert_ldm_original(args.checkpoint_path, args.config_path, args.output_path)


'''simple docstring'''




def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							)      ->       float:
    if digit_amount > 0:
        return round(number - int(UpperCamelCase__							)							,    UpperCamelCase__							)
    return number - int(UpperCamelCase__							)


if __name__ == "__main__":
  print(decimal_isolate(1.53, 0))
  print(decimal_isolate(35.345, 1))
  print(decimal_isolate(35.345, 2))
  print(decimal_isolate(35.345, 3))
  print(decimal_isolate(-14.789, 3))
  print(decimal_isolate(0, 2))
  print(decimal_isolate(-14.123, 1))
  print(decimal_isolate(-14.123, 2))
  print(decimal_isolate(-14.123, 3))
